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Nonlinear model predictive control from data: a set membership approach

Authors

  • M. Canale,

    Corresponding author
    1. Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi 24–10129 Torino, Italy
    • Correspondence to: M. Canale, Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi 24-10129 Torino, Italy.

      E-mail: massimo.canale@polito.it

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  • L. Fagiano,

    1. Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi 24–10129 Torino, Italy
    2. Department of Mechanical Engineering, University of California, Santa Barbara, CA, USA
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  • M.C. Signorile

    1. Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi 24–10129 Torino, Italy
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SUMMARY

A new approach to design a Nonlinear Model Predictive Control law that employs an approximate model, derived directly from data, is introduced. The main advantage of using such models lies in the possibility to obtain a finite computable bound on the worst-case model error. Such a bound can be exploited to analyze the robust convergence of the system trajectories to a neighborhood of the origin. The effectiveness of the proposed approach, named Set Membership Predictive Control, is shown in a vehicle lateral stability control problem, through numerical simulations of harsh maneuvers. Copyright © 2012 John Wiley & Sons, Ltd.

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